From emotion analysis and topic extraction to narrative modeling

Narrative economics studies the impact of popular narratives and stories on economic fluctuations in the context of human interests and emotions. Andreea Kremm and Mohammed Ibraaz Syed describe the use of emotion analysis, entity relationship extraction, and topic modeling in modeling narratives from written human communication.

Introduced formally by the renowned economist Robert Shiller in January 2017, narrative economics studies the impact of popular narratives and stories on economic fluctuations in the context of human interests and emotions. Professor Shiller is one of few economists to have predicted both the Great Recession of 2007–2009 and the dot-com crash. The term “narrative” refers to stories or ideas, particularly those of human interest and emotion that are spread through human communication. The human brain has been found to be highly attuned to narratives, whether factual or not, to justify ongoing decisions, actions, and even prejudices. Stories motivate and connect activities to deeply felt values and needs. Narrative modeling is determining the narrative from human communication. Simply put, a narrative model summarizes and categorizes the narrative and associated emotion parameters. Narrative modeling is more challenging compared to sentiment analysis—the determination of positive, negative, or neutral sentiment in a piece of text. Narrative modeling involves emotion analysis, topic modeling, ER (entity-relation) extraction, and subject modeling. Andreea Kremm and Mohammed Ibraaz Syed describe the use of emotion analysis, entity relationship extraction, and topic modeling in modeling narratives from written human communication and share some of the work they’ve done in these areas. Topics include: